Pike T, Mustard R A
Department of Surgery, Wellesley Hospital, University of Toronto, Ontario, Canada.
Comput Biol Med. 1992 May;22(3):173-9. doi: 10.1016/0010-4825(92)90013-d.
A data acquisition system that automatically discards corrupted or undesirable signals would save untold hours of drudgery for researchers. Continuous recording of variables to provide detailed behavior patterns generates huge amounts of raw data. Unfortunately waveforms usually require visual inspection for isolating desired behavior or validating signal integrity. This tedious and time-consuming step can potentially be eliminated using a novel computer science technique. We have trained a simulated neural network to recognize corrupted arterial pressure waveforms. Our system can now evaluate the validity of the arterial waveform without human intervention with an average false positive error rate of 2.2% and an average false negative error rate of 12.6%.
一个能自动丢弃损坏或不良信号的数据采集系统，将为研究人员节省难以计数的繁重工作时间。持续记录变量以提供详细行为模式会产生大量原始数据。不幸的是，波形通常需要目视检查以分离出所需行为或验证信号完整性。使用一种新颖的计算机科学技术有可能消除这一繁琐且耗时的步骤。我们训练了一个模拟神经网络来识别损坏的动脉压波形。我们的系统现在可以在无需人工干预的情况下评估动脉波形的有效性，平均误报错误率为2.2%，平均漏报错误率为12.6%。
